Tensor voting is a known technique of perceptually grouping unorganized and noisy data. However, the calculation of tensor voting schemes has typically been limited to special cases.
Many tensor voting schemes use precomputed voting fields as lookup tables from which votes are retrieved when necessary. While this system works well for many purposes, it requires large amounts of space for storing large amounts of voting fields, and any advantage of using pre-computed voting fields vanishes as the dimensionality increases. Applying this system to higher dimensionality becomes difficult. An application of the lookup table system to eight dimensions has been published, but further generalization to other dimensionality is unlikely.
Different forms of manifold learning techniques are known including those called locally linear embedding, Isomap, Laplacian Eigenmaps, Hessian LLE, semidefinite embedding, as well as other approaches.